Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

Abstract

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

“…An important contribution of the paper is that it demonstrates with two very different deployments how the same approach (same sensors, EKF parameters, and NLOS models) can be applied in totally different scenarios with the same rate of performance improvement. The need of learning or adaptation in the algorithms for different scenarios (common in many research papers [ 15 , 16 ]), is something that we were able to avoid, which is very convenient and practical in real life applications.…”

“…This kind of temporal in-range median-based solutions can circumvent the presence of sporadic outliers but fail when those errors are systematic. Other approaches that try to cancel outliers on the individual ranges, before the trilateration, are based on machine learning (ML) methods, such as k-nearest neighbors, Gaussian Processes, or Neural Networks [ 14 , 15 , 16 ]. However, methods based on learning are in many occasions invalid when changing the location site or if the conditions in the space change with time.…”

“…We will analyze the gain in accuracy obtained with the proposed multi-range (8-range) solution, and demonstrate how the same approach (same sensors, EKF parameters, and NLOS models) can be applied in totally different scenarios with the same rate of performance improvement, which is one of the contributions of the paper, apart from the proposal of a way to make possible the use of multiple ranges in one of the most used and successful pieces of UWB equipment in research labs and corporate solutions. The need of learning or adaptation in the algorithms for different scenarios (common in many research papers [ 14 , 15 , 16 ]) is something that we were able to avoid, which is very convenient and practical in real life applications.…”

Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar

“…The DWM1000 is the critical component of the self-positioning module. It provides the function of timestamping and transmission times precise controlling and can be used in the two-way ranging with an error within 10 cm [32][33][34][35][36]. Here, the two-way ranging is realized by the double-sided two-way ranging algorithm executed in the STM32 Microcontroller Unit (MCU) to achieve the self-positioning of the Golay-coded radars.…”

A New Dataset of People Flow in an Industrial Site with UWB and Motion Capture Systems

“…Cung Lian Sang et al [39] proposed an experimental dataset for multilabel classification results of a UWB ranging system. We proposed experimental data that reproduce the real localization of workers in an industrial environment.…”